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Massachusetts Institute of Technology

A framework for collecting data : revising sensor synchronization methods

Abstract

dc:description.abstract

Data collection is frequently carried out in research, as well as in industry for purposes ranging from quality control to assessing system limits. However, several complications may arise to hinder optimal data collection and analysis, including synchronization of different data types from a variety of sensors. A benchtop model was designed with the primary goal of understanding human-spacesuit interactions through the collection and analysis of force, pressure, and internal kinematics data. This thesis addresses shortcomings in the setup that led to difficulty in data analysis and synchronization and presents a revised framework for collecting these data. A system was designed such that the start of each trial of data collection can be synced across the three types of sensors: a load cell, a pressure mat, and inertial measurement units.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Gonzalez, Sara (Sara H.)
Advisor dc:contributor.advisor
  • Leia Stirling.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/112589
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/112589

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Gonzalez, Sara (Sara H.). A framework for collecting data : revising sensor synchronization methods. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112589